Neural control of autonomous vehicles

Lateral control of an autonomous road vehicle by a neural network is presented. The inputs into the controller such as relative vehicle position and yaw angle are delivered by dynamical video scene processing. Nonlinear conflicting requirements of safety and comfort have to be satisfied by the controller. The controller has been trained by the model-based training algorithm. In contrast to other neural network learning algorithms, it uses an explicit plant model to ensure fast and precise convergence. It does not require large training data sets-one or two representative initial states are mostly sufficient. Simulations and practical tests with speeds up to 80 km/h on public highways have confirmed the expectations